Competence models and the maintenance problem

Case-based reasoning (CBR) systems solve problems by retrieving and adaptin g the solutions to similar problems that have been stored previously as a c ase base of individual problem solving episodes or cases. The maintenance p roblem refers to the problem of how to optimize the performance of a CBR sy stem during its operational lifetime. It can have a significant impact on a ll the knowledge sources associated with a system (the case base, the simil arity knowledge, the adaptation knowledge, etc.), and over time, any one, o r more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutio ns focus on the maintenance of case knowledge by adding, deleting, or editi ng cases. This has lead to a renewed interest in the issue of case competen ce, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that u ltimately any generic maintenance solution must explicitly incorporate comp etence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case com petence that can provide a sound foundation for future maintenance solution s. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative a nd successful maintenance solutions to a variety of different maintenance p roblems.